1. Field of the Invention
The present invention relates to a method and an apparatus for measuring muscle fatigue, in which electromyographic (EMG) signals measured from a muscle are used to determine the fatigue level of the muscle.
2. Description of Prior Art
There are two basic methods to assess muscle fatigue: mechanical and electrical. Mechanical methods are based on measuring direct force output from muscle performance. This is, however, mostly unpractical and unreliable due to inability to separate each muscle component force from the total output force. Muscular systems have a strong tendency of compensating weak muscles with stronger ones in a constant dynamic fashion. In practice, by measuring single muscular contraction indirectly through muscle electrical signals (EMG), more precise conclusions can be drawn about the neurophysiological status of the muscle. Consequently, it has become widely accepted to deploy certain signal processing methods for EMG to gain information about muscle fatigue. In the following, a first exemplary technique to be discussed is based on estimation of spectral parameters, and a second technique to be discussed uses simple time domain signal processing.
In the first case, a measure of some form of average frequency (spectral shift) is calculated through the signal (EMG) spectrum. The original signal is sampled to produce a discrete time series, which is then subdivided into shorter segments of N samples each. For each segment spectral components (Fourier Spectrum) are estimated exploiting commonly known Fast Fourier Transform (FFT) algorithms. Average frequency calculations normally resort to power spectrum, which can be readily derived from the original Fourier spectrum. The two most popular frequency parameters used as fatigue descriptors are Mean Power Frequency (MPF) and Median Frequency (MF).
The second technique, called Zero Crossings (ZC), is a strongly simplified way of estimating average signal frequency in time domain, although it can be also defined through spectral calculations. Average intensity of rectified and smoothed EMG signal has also been correlated to some extent with muscle fatigue, but this has not gained as much popularity as the two other techniques.
In discrete form the two spectral parameters can be written by ##EQU1##
In the second technique (ZC) the time domain approach simply tries to determine the number of polarity changes in the signal during a given period of time. EQU ZC=.SIGMA.hd j[s( ).sub.j +s( ).sub.j ] (3)
where s( ).sub.j denotes the j.sup.th polarity change of the signal from negative to positive; and s( ).sub.j denotes the j.sup.th polarity change from positive to negative.
As mentioned earlier, there are ways of estimating ZC rates through spectral calculations, but they are rarely used due to slower processing times and inherent uncertanties as compared to direct time domain estimations.
Several problems emerge as these methods are applied to assess fatigue reflected in EMG signals. To achieve fast spectral estimations FFT algorithms are commonly used. This implies limiting the source time series segment to specific number of points, i.e. only groups of points having 2.sup.N elements (N is a positive integer) can be processed by FFT.
It has been suggested different solutions to circumvert these problems, but basically only few different size groups of points are allowed. This means that a huge number of varying length signal segments cannot be analyzed directly by FFT. The only known method to manage arbitrary number of points is the actual Discrete Fourier Transforms which is unpractical because of the much greater number of calculations required compared to FFT.
The basic problems with spectral estimations are, however, intrinsic. First of all, power spectrum is not unique. There is an unlimited number of different signals that can produce exactly the same power spectrum. Secondly, varying amounts of errors are always introduced to spectral estimations due to the finite number of temporal points and also due to different windowing functions used. Thirdly, the Fourier spectrum implies sinusoidal structural model for its target, which is rarely the case in physiological signals. These three factors to large extent can be credited to great amount of unspecificity and insensitivity found in many physiological signal analysis applications using spectral estimators.
Zero crossings methodology, on the other hand, does not account for any other information except the signal polarity. Therefore, any signal behaviour between two consequtive polarity changes will be left unnoticed. Since signal changes are mostly unpredictable in e.g. EMG, huge ammounts of information are ignored by using the ZC analysis method.